Prediction Market Scanner: Match Contract Rules

Give the Prediction Market Scanner a Defined Universe

Specify which venues, contract categories and lifecycle stages the scanner covers. A tool comparing open binary contracts has a different task from one studying historical settlement relationships. The universe determines which data must be current and what kinds of comparisons are meaningful.

Preserve venue and market identifiers from the start. A common title should not become the primary key. Polymarket International and Polymarket US are separate venue and account contexts, and similarly named markets across them should remain distinct records until the actual terms and operational requirements have been reviewed.

The older guide to multi market prediction market bot provides a broader context for working across instruments. A scanner can cover several markets while remaining read-only. Its ability to observe a contract does not establish that a particular account is eligible or authorized to trade it.

Treat Headline Similarity as Candidate Generation

Text similarity can efficiently identify markets worth comparing. It can recognize shared names, dates or event categories and reduce the amount of manual searching. Its role should stop at candidate generation until the exact contract terms are examined.

A scanner candidate pair should preserve why the match was proposed. Shared event name, overlapping date and similar threshold are useful clues, but each can be misleading. A market about whether an announcement occurs is not necessarily equivalent to one about whether the announced action takes effect.

Keep false matches as evaluation examples. They reveal which parts of the title-matching method need qualification. A scanner that reports only successful matches gives little evidence about its ability to reject attractive but incompatible pairs. Rejection quality matters because a false equivalence can make an ordinary price difference look like a guaranteed result.

Build a Structured Rule Record

For each contract, extract the event, outcome condition, threshold, units, observation period, time zone, designated source and payout definition. Add the treatment of revisions and exceptional events where the terms specify them. Preserve the original rule text or a reliable reference so extracted fields can be checked.

Kalshi's market outcome explanation identifies contract terms and information sources as the basis for determination. The scanner should therefore treat rule data as essential, not as optional descriptive text beneath a price feed.

Extraction can be incomplete or uncertain. If a field is not supported by the available terms, mark it unknown. A language model or parser should not fill gaps with customary assumptions and then present the result as verified. The structured record is a research aid whose claims must remain traceable to the actual instrument.

Run a Contract Equivalence Test

A contract equivalence test asks whether the compared positions have the same relevant payout conditions under the defined scope. Topic similarity is insufficient. The test should consider plausible states of the world in which the contracts could resolve differently, including boundary values and exceptional provisions.

For a hypothetical pair, Contract A pays Yes if Index Z is strictly above 200 in the first release. Contract B pays Yes if the revised value is at least 200. A first value of 200 followed by a revision to 201 makes the conditions differ. The pair fails a simple same-outcome test even though both headlines might say Index Z Above 200.

Record the counterexample that explains the failure. A concrete divergent scenario is more useful than a generic incompatible label. It allows the reader to understand the economic difference and determine whether a narrower comparative study still makes sense without pretending the contracts are interchangeable.

Check Thresholds, Rounding and Units

Threshold language matters near boundaries. Above, at least, below and no more than are not synonyms. A contract can also refer to a rounded published value or a more precise source value. The scanner should preserve the specified convention rather than applying its own preferred rounding rule.

Units can create less obvious mismatches. A percentage change, a percentage-point change and an absolute level describe different quantities. A title may abbreviate them in a way that looks similar. The structured rule record should make the measurement explicit before any numeric normalization occurs.

The older guide to prediction market odds bot helps with quote normalization, but normalization begins after instrument meaning is established. Converting two prices into percentages cannot repair incompatible thresholds or units. The scanner should identify the mismatch and stop any equivalence claim at that stage.

Compare Sources and Revision Policies

Two contracts can concern the same measurement but use different source agencies, publications or release versions. Those differences can matter when sources disagree, publish at different times or revise earlier values. The scanner should not assume that commonly similar data will always produce identical settlement.

For each source, record the exact role it plays. It may determine the final outcome, provide supporting context or serve as a fallback. A secondary article quoting the designated source is not necessarily a substitute for the source specified in the contract. Preserve that distinction in the compatibility record.

If revision policy is unclear, retain uncertainty. A pair can remain a research candidate without qualifying for a strict equivalence category. The guide to prediction market software discusses how tools should expose such missing prerequisites instead of replacing them with a reassuring score.

Align Time Windows and Time Zones

A contract about an event occurring by a deadline can differ from one about the event occurring during a narrower interval. Inclusive and exclusive boundaries matter. Time zones and daylight-saving transitions can also affect the actual instant represented by a written time, so the scanner needs a consistent, supported interpretation.

Preserve the original wording alongside any normalized timestamp. If a source lacks enough information to resolve the time unambiguously, mark the comparison incomplete. A guessed time zone can create a false match that remains hidden until an event occurs near the boundary.

A hypothetical announcement at 00:30 in one time zone may fall on the prior date in another. The scanner should compare actual defined intervals rather than calendar labels alone. The purpose is not to add complexity unnecessarily; it is to identify the specific boundary conditions that could make the instruments pay differently.

Inspect Exceptional and Unavailable-Data Cases

Ordinary outcomes may align while exceptional cases differ. Review postponement, cancellation, unavailable source data and any intermediate or alternative settlement provisions in the actual terms. Do not assume every Yes/No interface guarantees only a simple $1-or-$0 outcome under every circumstance.

A strict comparison should ask what happens when the named event does not occur as expected or the designated source fails to publish. If one contract uses a fallback and another follows a different determination rule, their economic relationship may change precisely when the market becomes difficult to interpret.

Preserve these cases in the resolution compatibility record. A reader can then distinguish equivalent under ordinary assumptions from equivalent across the specified contractual states. The former may support research, but it should not be advertised as a guarantee that paired positions eliminate all outcome or settlement risk.

Separate Lifecycle Compatibility From Rule Compatibility

Two contracts can have matching outcome definitions while being in different operational states. One may still accept orders, another may be closed awaiting determination and a third may already be settled. The scanner should not rank all three as current trading opportunities merely because their rules align.

The guide to a prediction market scanner bot develops lifecycle transitions. A rule comparison can remain valid for historical analysis after trading closes, while an execution comparison becomes unavailable. Keep those uses separate so the system does not discard useful research or imply unavailable action.

Record the lifecycle observation time and source. A stale open status can be as misleading as a stale price. When the status cannot be verified, the comparison should show that limitation explicitly. The user needs to know whether the missing prerequisite concerns contract meaning, current tradability or both.

Synchronize Quotes Before Interpreting a Gap

After compatibility is established, compare prices from sufficiently aligned observations. One venue's current ask and another's old bid can create a gap that never existed at the same time. A mathematically correct subtraction does not make the comparison executable.

Preserve quote time, receipt time, side and quantity. Define an acceptable age difference for the intended task. If one side is stale, the scanner can retain the pair as a research match while withholding the current gap claim. Do not silently carry forward an old value and present it as fresh.

The guide to prediction market alerts helps turn comparisons into useful notifications. An alert should distinguish a new compatible pair, a changed price gap and a data-quality problem. Those events need different responses and should not all appear as an opportunity message.

Compare the Relevant Sides and Quantities

The displayed price may be a midpoint or last trade. A proposed purchase uses available asks under the venue's representation, while a sale uses relevant bids. The scanner should calculate the scenario for the intended quantities and current depth rather than subtract two attractive display values.

Suppose two hypothetical compatible simple contracts offer a Yes position at $0.46 and a complementary No position at $0.51. Their quoted sum is $0.97 before costs. That arithmetic alone does not establish a guaranteed three-cent result. The quoted quantities may differ, one leg may not fill and the contracts' full compatibility still needs to hold.

If an illustrative combined fee is $0.02 per completed pair, the modeled difference becomes $0.01 under the stated payout assumptions. Funding, execution and settlement constraints can still matter. The example explains why costs and quantities belong in the record; it is not a claim that such a pair is currently available or risk-free.

Account for Partial Execution Explicitly

A multi-leg proposal can become a one-sided position if only one leg fills. The scanner's research output should identify this possibility before a downstream system considers action. A price relationship on a screen does not guarantee simultaneous execution or equal quantities.

For a hypothetical target of 100 paired units, 100 fills on one side and 40 on the other leave 60 unmatched units under the assumed construction. The remaining exposure must be understood and managed under a separate authorized policy. Calling the original proposal arbitrage does not remove that operational reality.

The older guide to prediction market arbitrage bot explains the importance of verifying every leg. A scanner can support that workflow by passing exact identifiers, compatible rule evidence and quantity constraints. It should not imply that finding the pair completes the economic transaction.

Preserve the Reason for Every Disposition

A pair can qualify under the defined scope, fail because of a specific mismatch or remain unresolved because essential terms are missing. Keep these states separate. An unresolved pair is not necessarily incompatible, and a rejected equivalence claim does not mean the markets are unrelated for every research purpose.

Record the primary reason and any secondary issues. A pair may differ in both source and deadline. The reader should be able to see all material differences without a single score hiding them. A concrete explanation also helps determine whether a future clarification justifies reopening the comparison.

Do not erase failed candidates after review. They are useful examples for improving extraction and matching. A system evaluated only on accepted pairs can appear precise while generating a large burden of misleading candidates. The quality of the scanner includes how efficiently it identifies and explains nonmatches.

Test Boundary Cases Before Broad Coverage

Construct a small acceptance set with ordinary matches and deliberate near misses. Include strictly above versus at least, initial versus revised values, different time zones, unavailable-data provisions and matching rules with incompatible lifecycle states. The expected disposition should be written before the scanner processes the cases.

Add a synchronized-price test and a stale-price test for the same compatible pair. The rule result should remain stable while the current gap result changes according to data freshness. This demonstrates that the system keeps instrument meaning and market observation as separate layers.

Ask a reviewer to reconstruct the result from the saved evidence. They should be able to identify the decisive term or timestamp without relying on the model's explanation alone. A useful scanner makes verification easier; it does not require the reader to trust an opaque assertion that two contracts are the same.

Keep Human Review Focused on the Hard Cases

Automation can handle repetitive extraction and comparison while routing ambiguous terms to review. The review queue should state the precise unresolved issue, such as a missing revision policy or unclear fallback source. A vague low-confidence label makes the reviewer repeat the entire analysis.

Prioritize cases according to research relevance and available capacity. A large apparent price gap may justify attention, but it should not override a fundamental identity or rule problem. The queue can preserve the gap as a reason to investigate while withholding any claim that the pair is actionable.

Record the reviewer's conclusion and evidence. If a manual interpretation becomes a reusable rule, version it and test it against prior counterexamples. This prevents one convenient resolution from silently changing the scanner's treatment of a whole category of contracts whose terms may differ in important ways.

Revisit Matches After a Rule Clarification

A compatibility record should have a review trigger when official terms or clarifications change. The scanner needs to identify which extracted fields are affected and which accepted pairs depend on them. A price feed can remain healthy while the meaning of the instrument requires renewed review. Treating rules as permanent metadata can leave the system comparing contracts under an outdated assumption.

Preserve the earlier conclusion and the evidence that supported it. A later clarification may confirm the original interpretation, narrow it or reveal a material difference. The historical record should show what the scanner knew at each stage. This is especially useful when a researcher evaluates whether an earlier alert was reasonable under the information available then.

Do not automatically replace all historical rule records with the latest version. Current comparisons should use the currently applicable terms, while retrospective studies need the appropriate historical context. If that context cannot be established, label the limitation. A clean archive with silently rewritten meanings can be less useful than an imperfect archive that honestly preserves uncertainty.

The same review process can apply when an extraction method changes. A newly recognized clause may alter compatibility without any change to the contract itself. The scanner should identify that as an interpretation update, helping the reader distinguish improved analysis from a venue changing the instrument.

A Prediction Market Scanner Should Explain the Match

The completed output should answer why the contracts are being compared, which terms align, which limitations remain and what current market observations support the displayed gap. A familiar headline and two prices are insufficient. The useful result is a traceable relationship between instruments and evidence.

The hypothetical examples show how small wording differences can defeat a same-outcome assumption and how execution constraints remain even when rules align. A scanner adds value by making these distinctions easier to inspect, not by labeling every unusual price relationship a guaranteed opportunity.

Build the workflow in order: candidate generation, rule comparison, lifecycle review and synchronized market analysis. Preserve rejected and uncertain cases alongside accepted ones. That produces a research instrument whose conclusions can be challenged and updated, while keeping any later trading authorization and execution responsibility separate.